Batch Shipyard and Pool Autoscaling

The focus of this article is to describe the Azure Batch Pool autoscaling feature and how Batch Shipyard exposes the functionality.

Azure Batch Pool Autoscale

Azure Batch has the ability to automatically scale your pool up or down depending upon criteria that you specify. Remember, that you are only charged in Azure Batch for the compute resources that are used (e.g., VM hours, disk and network egress), thus by automatically scaling your resources on-demand, you can use Azure resources more efficiently and potentially reduce your costs.

Azure Batch exposes metrics that can be queried as historical sample data. These metrics include usage information such as CPU, memory and disk as well as node counts and task counts. These metrics can then be used to determine what the proper target node counts should be. The logic for what the target node counts should be is expressed as an autoscale formula.

Azure Batch periodically evaluates an autoscale formula as specified by the evaluation interval timespan which can be as frequent as every 5 minutes. If the evaluated target node counts are different after evaluation, the pool automatically resizes to these target node counts without any intervention needed by the user.

For more information about Azure Batch Autoscale, please visit this document.

Batch Shipyard and Pool Autoscale

Batch Shipyard exposes pool autoscale functionality in the pool configuration file under the property autoscale. There are two approaches to specifying how to apply autoscale to a compute pool. Scenario-based autoscaling is a simple way to specify autoscale for a pool without needing to be an expert in creating an autoscale formula. There are named autoscale scenarios that you can select from. Formula-based autoscaling is for users that want to specify their own custom autoscale formula.

Scenario-based Autoscaling

Scenario-based autoscaling allows you to pick from a set of common autoscale scenarios and then Batch Shipyard automatically applies the appropriate transformations to the formula to apply to your pool. These scenarios are:

  • active_tasks will autoscale the pool using metrics for the number of active tasks for the pool. Tasks categorized under this metric are tasks in active state with satisifed dependencies, in other words, "tasks with satisified dependencies awaiting node assignment".
  • pending_tasks will autoscale the pool using metrics for the number of pending tasks for the pool. Tasks categorized under this metric are tasks in active state with satisifed dependencies and running tasks, in other words, "tasks pending completion".
  • workday will autoscale the pool according to Monday-Friday workdays.
  • workday_with_offpeak_max_low_priority will autoscale the pool according to Monday-Friday workdays and for off work time, use maximum number of low priority nodes.
  • weekday will autoscale the pool if it is a weekday.
  • weekend will autoscale the pool if it is a weekend.

You can specify a scenario-based autoscale on a pool by populating the property autoscale:scenario:name with one of the scenarios above.

You will also need to specify a autoscale:scenario:maximum_vm_count property which can contain both dedicated and low_priority counts to ensure that the formulas cannot evaluate to a target node count higher than some threshold that you specify. Specifying a negative value for the count will effectively set the maximum to no limit. Note that the vm_count specified at the pool_specification level are automatically inferred as minimum VM counts. These counts can be set to 0 to allow the pool to resize down to zero nodes.

Additionally, there are options that can modify and fine-tune these scenarios as needed:

  • node_deallocation_option which specify when a node is targeted for deallocation but has a running task, what should be the action applied to the task: requeue, terminate, taskcompletion, and retaineddata. Please see this doc for more information about these options. This option applies to all scenarios.
  • sample_lookback_interval is the time interval to lookback for past history. This option applies only to active_tasks and pending_tasks scenarios.
  • required_sample_percentage is the required percentage of samples that must be present during the sample_lookback_interval. This option applies only to active_tasks and pending_tasks scenarios.
  • bias_last_sample will bias the autoscale scenario to use the last sample during history computation and metric weighting. This can be enabled to more quickly respond to changes in history with respect to averages. This option applies only to active_tasks and pending_tasks scenarios.
  • bias_node_type will bias the the autoscale scenario to favor one type of node over the other when making a decision on how many of each node to allocate. By default, allocation is equal-weighted but can be selected to favor either dedicated or low_priority. This applies to all scenarios.
  • rebalance_preemption_percentage will rebalance the compute nodes to bias for dedicated nodes when the pre-empted node count reaches the indicated threshold percentage of the total current dedicated and low priority nodes. This applies only to active_tasks and pending_tasks scenarios.

An example autoscale specification in the pool configuration may be:

    evaluation_interval: 00:05:00
      name: active_tasks
        dedicated: 16
        low_priority: 8

This example would apply the active_tasks scenario to the associated pool with an evaluation interval of every 5 minutes. This means that the autoscale formula is evaluated by the service and can have updates applied every 5 minutes. Note that having a small evaluation interval may result in undesirable behavior of the pool being resized constantly (or even resize failures if the prior resize is still ongoing when the autoscale evaluation happens again and results in a different target node count). The active_tasks scenario also includes a maximum_vm_count to ensure that the autoscale formula does not result in target node counts that exceed 16 dedicated and 8 low priority nodes.

Formula-based Autoscaling

Formula-based autoscaling allows users with expertise in creating autoscale formulas to create their own formula and apply it to a Batch Shipyard pool. These formulas should be specified on the formula member within the autoscale property.

For more information about how to create your own custom autoscale formula, please visit this document.